Anomaly Detection in Autonomous Driving: A Survey

April 17, 2022 Β· Declared Dead Β· πŸ› 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Authors Daniel Bogdoll, Maximilian Nitsche, J. Marius ZΓΆllner arXiv ID 2204.07974 Category cs.RO: Robotics Citations 170 Venue 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Last Checked 2 months ago
Abstract
Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set conditions, they still struggle to handle the unexpected. This survey provides an extensive overview of anomaly detection techniques based on camera, lidar, radar, multimodal and abstract object level data. We provide a systematization including detection approach, corner case level, ability for an online application, and further attributes. We outline the state-of-the-art and point out current research gaps.
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